Algorithm for optimizing the model using transformer. حسب Weiling Liu (1465480)

Summary of the feature selection algorithms. حسب Martin Kostadinov (20410120)

Comparative Internal and External AUC Performance Across Single-Site Models. حسب Ioana Duta (18462981)

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Performance of incremental models. حسب Ioana Duta (18462981)

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Performance of LOSO models. حسب Ioana Duta (18462981)

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Dataset breakdown. حسب Ioana Duta (18462981)

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Median and IQR of the model AUC values across each study site for internal and external validation. حسب Ioana Duta (18462981)

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Variation of model weights between sites. حسب Ioana Duta (18462981)

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Effect of changing thresholds on accuracy. حسب Ioana Duta (18462981)

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Histogram showing propensity scores as calculated from the processed data, coloured by whether there was a diagnosis of epilepsy (blue) or not (red). حسب Ioana Duta (18462981)

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Covariate values per site. حسب Ioana Duta (18462981)

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PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. حسب Yuxi Long (11024307)

"…<p>^<b>a</b>PATH⁺ shows comparable or better performance with less overfitting, as evidenced by a smaller slope, with much less increase in RMSEs beyond the training dataset, compared to established binding affinity prediction algorithms spanning a variety of methods. The benchmarked algorithms include physics-based and deep learning algorithms from the famous AutoDock framework (scoring function of AutoDock4 implemented in the AutoDockFR package [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref068" target="_blank">68</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref077" target="_blank">77</a>], Vinardo [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref069" target="_blank">69</a>], GNINA [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref070" target="_blank">70</a>]), empirical (AA-Score [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref071" target="_blank">71</a>]), knowledge-based (SMoG2016 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref072" target="_blank">72</a>]), and deep learning-based scoring functions (OnionNet [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref073" target="_blank">73</a>], PLANET [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013216#pcbi.1013216.ref074" target="_blank">74</a>]). …"

Summary of major algorithms used for signal detection. حسب Feilong Tan (14886395)

Data propagation algorithm stages. حسب Fazia Akhtar (21453122)

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Learning dynamics on training tasks. حسب Sam Hall-McMaster (10343795)

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The data used in the figures. حسب Yonghui Zhang (279832)

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Data Sheet 1_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf حسب Habtamu Setegn Ngusie (12178327)

"…A comparative analysis of 12 advanced machine learning algorithms was conducted, utilizing various data balancing techniques and hyperparameter optimization methods to enhance model performance.…"

Data Sheet 2_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf حسب Habtamu Setegn Ngusie (12178327)

"…A comparative analysis of 12 advanced machine learning algorithms was conducted, utilizing various data balancing techniques and hyperparameter optimization methods to enhance model performance.…"

Statistical data for the dataset. حسب Rizk M. Rizk-Allah (18625751)

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Power generation data. حسب Rizk M. Rizk-Allah (18625751)

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